{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from simtax import person, hhold, simtax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "husb = person(age=65,earn=30e3)\n",
    "wife = person(age=65,earn=10e3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "hh = hhold(husb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "this = simtax(year=2016)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "this.file(hh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "earns = np.linspace(0.0,250e3,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "mtrs = []\n",
    "for e in earns:\n",
    "    hh.sp[0].inc_earn = e\n",
    "    mtrs.append(this.hmtr(hh,0,1e3))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d71a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.figure()\n",
    "plt.plot(earns,mtrs)\n",
    "plt.xlabel('taxable income of person 1')\n",
    "plt.ylabel('household marginal tax rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
